CVE-2026-28220 (GCVE-0-2026-28220)
Vulnerability from cvelistv5 – Published: 2026-07-20 15:26 – Updated: 2026-07-20 17:30
VLAI
EPSS
VEX
Title
Wazuh cluster DAPI arbitrary callable deserialization and RBAC context injection allow a cluster peer to execute privileged functions on the master node
Summary
Wazuh is a free and open source platform used for threat prevention, detection, and response. Prior to version 4.14.5, issues in the Cluster Distributed API (DAPI) handling allow a cluster peer, or any actor able to authenticate to the cluster channel using the shared cluster key, to make the master node deserialize an attacker-controlled callable and execute it under an attacker-controlled RBAC context. The cluster code in `framework/wazuh/core/cluster/common.py` deserializes JSON with `as_wazuh_object()`, which resolves any callable whose top-level package is wazuh or api (an overly broad allowlist controlled only by `ALLOWED_CALLABLES_PACKAGES`), and DAPI requests handled in framework/wazuh/core/cluster/dapi/dapi.py accept a client-supplied rbac_permissions value that `run_local()` applies as the global RBAC context, so supplying an rbac_mode of black causes authorization checks for expose_resources-protected functions to pass without any legitimate permission assignment. Combined, these allow privileged administrative actions on the master node such as arbitrary file writes under WAZUH_PATH, creation of new API users, and tampering with security.yaml, and can be chained into full manager compromise. This issue has been fixed in version 4.14.5.
Severity
8.4 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-20 17:30 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/wazuh/wazuh/security/advisorie… | x_refsource_CONFIRM |
Impacted products
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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